Least Bit Error Rate Adaptive Nonlinear Equalizers for Binary Signalling
Least Bit Error Rate Adaptive Nonlinear Equalizers for Binary Signalling
The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural network equalizers for binary signalling. Motivated from a kernel density estimation of the bit error rate (BER) as a smooth function of training data, a stochastic gradient algorithm called the least bit error rate (LBER) is developed for adaptive nonlinear equalizers. This LBER algorithm is applied to adaptive training of a radial basis function (RBF) equalizer in a channel intersymbol interference (ISI) plus co-channel interference setting. Simulation study shows that the proposed algorithm has a good convergence speed and a small-size RBF equalizer trained by the LBER can closely approximate the performance of the optimal Bayesian equalizer. The results also demonstrates that the standard adaptive algorithm, the least mean square (LMS), performs poorly for neural network equalizers, due to the reason that the minimum mean square error (MMSE) is irrelevant to the equalization goal.
29-36
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
February 2003
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Mulgrew, B.
95a3fbda-7de2-4583-b1f2-0a54a69b414a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S., Mulgrew, B. and Hanzo, L.
(2003)
Least Bit Error Rate Adaptive Nonlinear Equalizers for Binary Signalling.
IEE Proceedings Communications, 150 (1), .
Abstract
The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural network equalizers for binary signalling. Motivated from a kernel density estimation of the bit error rate (BER) as a smooth function of training data, a stochastic gradient algorithm called the least bit error rate (LBER) is developed for adaptive nonlinear equalizers. This LBER algorithm is applied to adaptive training of a radial basis function (RBF) equalizer in a channel intersymbol interference (ISI) plus co-channel interference setting. Simulation study shows that the proposed algorithm has a good convergence speed and a small-size RBF equalizer trained by the LBER can closely approximate the performance of the optimal Bayesian equalizer. The results also demonstrates that the standard adaptive algorithm, the least mean square (LMS), performs poorly for neural network equalizers, due to the reason that the minimum mean square error (MMSE) is irrelevant to the equalization goal.
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sqc-bm-lh-IEEproc-Feb03.pdf
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Published date: February 2003
Additional Information:
submitted for publication in Jan. 2001, revised in Feb. 2002
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 257324
URI: http://eprints.soton.ac.uk/id/eprint/257324
ISSN: 1350-2425
PURE UUID: 1cd6c0e0-19a1-465c-a3c3-b7008266454c
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Date deposited: 18 Nov 2003
Last modified: 18 Mar 2024 02:33
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Contributors
Author:
S. Chen
Author:
B. Mulgrew
Author:
L. Hanzo
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